
'''simple docstring'''







from __future__ import annotations

from typing import Any


class 		__UpperCAmelCase :


							def __init__(			self       ,  lowerCAmelCase_    ):





								"""simple docstring"""






								_snake_case  		=						num_of_nodes
								_snake_case  		=						[]
								_snake_case  		=						{}


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_    ):





								"""simple docstring"""






								self.m_edges.append([u_node, v_node, weight]    )


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_    ):





								"""simple docstring"""






								if self.m_component[u_node] == u_node:
									return u_node
								return self.find_component(self.m_component[u_node]    )


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_    ):





								"""simple docstring"""






								if self.m_component[u_node] != u_node:
									for k in self.m_component:
										_snake_case  		=						self.find_component(lowerCAmelCase_    )


							def 					lowerCamelCase							(			self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_    ):





								"""simple docstring"""






								if component_size[u_node] <= component_size[v_node]:
									_snake_case  		=						v_node
									component_size[v_node] += component_size[u_node]
									self.set_component(lowerCAmelCase_    )

								elif component_size[u_node] >= component_size[v_node]:
									_snake_case  		=						self.find_component(lowerCAmelCase_    )
									component_size[u_node] += component_size[v_node]
									self.set_component(lowerCAmelCase_    )




							def 					lowerCamelCase							(			self    ):





								"""simple docstring"""






								_snake_case  		=						[]
								_snake_case  		=						0

								_snake_case  		=						[-1] * self.m_num_of_nodes

								# A list of components (initialized to all of the nodes)
								for node in range(self.m_num_of_nodes    ):
									self.m_component.update({node: node}    )
									component_size.append(1    )

								_snake_case  		=						self.m_num_of_nodes

								while num_of_components > 1:
									for edge in self.m_edges:
										_snake_case		,					_snake_case		,					_snake_case  		=						edge

										_snake_case  		=						self.m_component[u]
										_snake_case  		=						self.m_component[v]

										if u_component != v_component:

											for component in (u_component, v_component):
												if (
												    minimum_weight_edge[component] == -1
												    or minimum_weight_edge[component][2] > w
												):
													_snake_case  		=						[u, v, w]

									for edge in minimum_weight_edge:
										if isinstance(lowerCAmelCase_       ,  lowerCAmelCase_    ):
											_snake_case		,					_snake_case		,					_snake_case  		=						edge

											_snake_case  		=						self.m_component[u]
											_snake_case  		=						self.m_component[v]

											if u_component != v_component:
												mst_weight += w
												self.union(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_    )
												print(F'Added edge [{u} - {v}]\nAdded weight: {w}\n'    )
												num_of_components -= 1

									_snake_case  		=						[-1] * self.m_num_of_nodes
								print(F'The total weight of the minimal spanning tree is: {mst_weight}'    )







def 							SCREAMING_SNAKE_CASE__      (  )			->      str:
	pass






if __name__ == "__main__":
					import doctest

					doctest.testmod()








"""simple docstring"""





import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 						lowercase   (   unittest.TestCase   ):





      @property
      def        _snake_case     (			self					)						->	List[str]:
            torch.manual_seed(0					)
            lowerCAmelCase    		=			UNetaDModel(
                block_out_channels=(32, 64) ,		layers_per_block=2 ,		sample_size=32 ,		in_channels=3 ,		out_channels=3 ,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,		)
            return model





      def        _snake_case     (			self					)						->	List[Any]:
            lowerCAmelCase    		=			self.dummy_uncond_unet
            lowerCAmelCase    		=			ScoreSdeVeScheduler()

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=2 ,		output_type="""numpy""" ,		generator=lowercase ,		return_dict=lowercase					)[
                0
            ]

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]
            lowerCAmelCase    		=			image_from_tuple[0, -3:, -3:, -1]

            assert image.shape == (1, 32, 32, 3)
            lowerCAmelCase    		=			np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
            assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2







@slow
@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			"""google/ncsnpp-church-256"""
            lowerCAmelCase    		=			UNetaDModel.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVeScheduler.from_pretrained(lowercase					)

            lowerCAmelCase    		=			ScoreSdeVePipeline(unet=lowercase ,		scheduler=lowercase					)
            sde_ve.to(lowercase					)
            sde_ve.set_progress_bar_config(disable=lowercase					)

            lowerCAmelCase    		=			torch.manual_seed(0					)
            lowerCAmelCase    		=			sde_ve(num_inference_steps=10 ,		output_type="""numpy""" ,		generator=lowercase					).images

            lowerCAmelCase    		=			image[0, -3:, -3:, -1]

            assert image.shape == (1, 256, 256, 3)

            lowerCAmelCase    		=			np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]					)

            assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2






